Overview

Dataset statistics

Number of variables3
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory756.0 B
Average record size in memory31.5 B

Variable types

Numeric2
Categorical1

Dataset

Description공무원 정원 현황입니다. 항목은 기준연도, 소속시도 구분, 인원수입니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=150

Alerts

인원수 is highly overall correlated with 소속시도 구분High correlation
소속시도 구분 is highly overall correlated with 인원수High correlation
인원수 has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:45:37.762190
Analysis finished2024-01-09 21:45:38.218983
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Real number (ℝ)

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5
Minimum2010
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:45:38.260450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010.15
Q12012.75
median2015.5
Q32018.25
95-th percentile2020.85
Maximum2021
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.5262987
Coefficient of variation (CV)0.00174959
Kurtosis-1.2156934
Mean2015.5
Median Absolute Deviation (MAD)3
Skewness0
Sum48372
Variance12.434783
MonotonicityNot monotonic
2024-01-10T06:45:38.349922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2010 2
8.3%
2011 2
8.3%
2012 2
8.3%
2013 2
8.3%
2014 2
8.3%
2015 2
8.3%
2016 2
8.3%
2017 2
8.3%
2018 2
8.3%
2019 2
8.3%
Other values (2) 4
16.7%
ValueCountFrequency (%)
2010 2
8.3%
2011 2
8.3%
2012 2
8.3%
2013 2
8.3%
2014 2
8.3%
2015 2
8.3%
2016 2
8.3%
2017 2
8.3%
2018 2
8.3%
2019 2
8.3%
ValueCountFrequency (%)
2021 2
8.3%
2020 2
8.3%
2019 2
8.3%
2018 2
8.3%
2017 2
8.3%
2016 2
8.3%
2015 2
8.3%
2014 2
8.3%
2013 2
8.3%
2012 2
8.3%

소속시도 구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
도
12 
시군
12 

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도
2nd row도
3rd row도
4th row도
5th row도

Common Values

ValueCountFrequency (%)
도 12
50.0%
시군 12
50.0%

Length

2024-01-10T06:45:38.443944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:45:38.516819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도 12
50.0%
시군 12
50.0%

인원수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9074.875
Minimum3823
Maximum15753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T06:45:38.585476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3823
5-th percentile3890.3
Q14348.5
median9435
Q313028.5
95-th percentile14763.2
Maximum15753
Range11930
Interquartile range (IQR)8680

Descriptive statistics

Standard deviation4643.1648
Coefficient of variation (CV)0.51165055
Kurtosis-1.9897592
Mean9074.875
Median Absolute Deviation (MAD)4404
Skewness0.027003981
Sum217797
Variance21558979
MonotonicityNot monotonic
2024-01-10T06:45:38.682752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3887 1
 
4.2%
12945 1
 
4.2%
15753 1
 
4.2%
14807 1
 
4.2%
14515 1
 
4.2%
13855 1
 
4.2%
13463 1
 
4.2%
13162 1
 
4.2%
12984 1
 
4.2%
12900 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
3823 1
4.2%
3887 1
4.2%
3909 1
4.2%
3977 1
4.2%
4052 1
4.2%
4311 1
4.2%
4361 1
4.2%
4362 1
4.2%
5047 1
4.2%
5423 1
4.2%
ValueCountFrequency (%)
15753 1
4.2%
14807 1
4.2%
14515 1
4.2%
13855 1
4.2%
13463 1
4.2%
13162 1
4.2%
12984 1
4.2%
12945 1
4.2%
12900 1
4.2%
12828 1
4.2%

Interactions

2024-01-10T06:45:37.991286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:45:37.840479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:45:38.056766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:45:37.919655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:45:38.748111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도소속시도 구분인원수
기준연도1.0000.0000.000
소속시도 구분0.0001.0001.000
인원수0.0001.0001.000
2024-01-10T06:45:38.816015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도인원수소속시도 구분
기준연도1.0000.4740.000
인원수0.4741.0000.905
소속시도 구분0.0000.9051.000

Missing values

2024-01-10T06:45:38.139809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:45:38.195209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기준연도소속시도 구분인원수
02010도3887
12011도3909
22012도3823
32013도3977
42014도4052
52015도4311
62016도4361
72017도4362
82018도5047
92019도5423
기준연도소속시도 구분인원수
142012시군12480
152013시군12676
162014시군12900
172015시군12984
182016시군13162
192017시군13463
202018시군13855
212019시군14515
222020시군14807
232021시군15753